MDC-Kace: A Model for Predicting Lysine Acetylation Sites Based on Modular Densely Connected Convolutional Networks

Lysine acetylation (Kace) is a conservative protein posttranslational modification (PTM) closely related to various metabolic diseases. Therefore, Kace sites identification is of great significance for investigating metabolic disease treatments. Existing studies have shown that protein structural properties contain highly useful structural information, which provides a strong basis for identifying PTMs. During the feature learning process, features at different levels are complementary, and taking them into consideration can effectively improve the quality of the features. However, existing deep-learning methods used protein sequence-level information as input without considering the protein structural properties. Furthermore, only high-level features were focused on, resulting in considerable information loss and weakening the prediction results. Therefore, we propose a novel deep-learning model based on modular densely connected convolutional networks (MDC) for Kace sites prediction, called MDC-Kace. MDC-Kace introduces the protein structural properties and combines them with the original protein sequence and the amino acid physicochemical properties to construct the site's feature space. Then, modular densely connected convolutional networks are used to capture the information of features at different levels and reduce information loss and crosstalk during the feature learning step. We add a squeeze-excitation layer to evaluate the importance of different features and improve the network abstraction ability to identify potential Kace sites. The experimental results of ten-fold cross-validation and independent testing in human, Mus musculus and Escherichia coli datasets showed that our MDC-Kace model outperforms the existing Kace site predictors and can predict potential Kace sites effectively. MDC-Kace can be available at https://github.com/lianglianggg/MDC-Kace.